Search Results (64)

Twin Rotor Multi-Input Multi-Output (TRMS) platforms combine strong aerodynamic cross-coupling, gravitational loading on the vertical axis, friction-dominated horizontal dynamics, and systematic mismatch between idealised models and laboratory hardware. The platform provides only two optical encoders, so the angular rates and the rotor states are unavailable for feedback. This paper presents an observer-based output-feedback architecture that addresses both difficulties. A high-gain observer built on the fully coupled six-state model, including the gyroscopic terms that the control design cannot retain, reconstructs the four unmeasured states from the two encoder angles. The reconstructed states drive a Hybrid Backstepping–Super-Twisting (B-STA) controller in which a second-order continuous sliding mode is embedded at the final recursive step through a composite surface. Because backstepping requires strict-feedback structure, which the centralised coupled model does not possess, the controller is synthesised on a decentralised design model and the residual coupling is rejected as a matched perturbation of the sliding variable. Closed-loop behaviour is analysed as a three-stage cascade covering observer error, sliding variable, and tracking error, yielding practical stability under bounded residuals with an explicit input-to-state gain. The residual bounds are evaluated numerically from the actuator saturation limit and the identified coefficients rather than assumed, and the resulting figures are shown to predict the marked difference in sliding-variable behaviour observed between the two axes. A second architecture applies a windowed Grey Wolf Optimiser (B-GWO) to the backstepping gains, in which each candidate is applied to the plant for a fixed test window, scored on its own accumulated integral of time-weighted absolute error, and followed by a settle window at the incumbent best. We prove that this windowing is a requirement rather than a convenience: a fitness evaluated at a single sample is common to all candidates, cancels from the population ranking, and reduces the search to the minimiser of its own regularisation term. Both schemes are implemented on a physical TRMS through a Simulink Desktop Real-Time interface at a control period of $10\mathrm{ms}$. On a $100\mathrm{s}$ experimental run, B-STA attains a pitch tracking error of $0.0318\mathrm{rad}$ RMS, $7.94\%$ of the reference amplitude, and the lowest control energy on both axes among the strategies compared, reducing pitch control energy by $72.9\%$ relative to a first-order Backstepping–Sliding Mode baseline recorded on the same interface. Numerical disturbance rejection tests on the fully coupled model confirm the mechanism: under a matched actuator step the super-twisting integrator state migrates to a new steady level that cancels the disturbance, driving the residual pitch error to $2\times {10}^{-4}\mathrm{rad}$, whereas the same recursive law without the second-order injection retains a permanent offset of $0.28\mathrm{rad}$. Full article

Nonlinear biological systems exhibit complex interactions, uncertain parameters, and strong treatment-dependent dynamics, making mathematical modeling and control essential for designing reliable therapeutic intervention strategies. This study proposes a multi-input adaptive smooth sliding mode control (AMIS-SMC) framework for regulating combined radiotherapy and chemotherapy in a nonlinear tumor–immune dynamical system described by ordinary differential equations. The proposed controller integrates a hyperbolic tangent smoothing mechanism with adaptive parameter-estimation laws to compensate for uncertainty in tumor and healthy-cell growth dynamics. In this way, the method explicitly links biological-system modeling, feedback control, treatment-dose management, and parameter adaptation within a single mathematically analyzable framework. Fundamental closed-loop properties are established analytically, including positivity and boundedness of all biological state variables, asymptotic convergence of the sliding surfaces, and explicit upper bounds on the administered radiation and chemotherapeutic drug dosages. Lyapunov-based stability analysis is used to guarantee boundedness of the closed-loop signals and convergence of the sliding manifold. Numerical simulations based on a brain-tumor case study demonstrate that the proposed AMIS-SMC algorithm achieves rapid tumor suppression while administering substantially lower treatment intensities than conventional and integral SMC approaches. Under nominal conditions, the proposed controller reduces cumulative radiation and chemotherapy dosages while maintaining effective tumor mitigation. Under mismatched parameter conditions, AMIS-SMC consistently drives the tumor-cell population toward the desired equilibrium across all tested scenarios, whereas conventional and integral SMC show limited adaptability. Statistical analysis using Mann–Whitney U and Fisher’s tests further indicates that AMIS-SMC provides an effective mathematical-control and treatment-management strategy for tumor suppression in a simplified nonlinear tumor–immune biological system under parameter uncertainty. Full article

To address high-precision trajectory tracking challenges in multi-joint robots facing model uncertainties, complex friction, and abrupt disturbances, this paper proposes a radial basis function (RBF) neural network-improved integral terminal sliding mode control scheme optimized by state-aware adaptive particle swarm optimization (ASPSO), denoted as ASPSO-optimized RBF-IITSMC. First, a fractional-memory integral terminal sliding surface incorporating a boundary-layer saturation mapping is constructed. The proposed terminal mapping is shown to be globally Lipschitz continuous, and an explicit approximation-error bound relative to the conventional terminal power mapping is established. Second, an RBF neural compensator driven by the sliding variable is incorporated into the reconstructed sliding dynamics to estimate lumped uncertainties and reduce the compensation burden on the robust feedback term. Furthermore, a state-aware adaptive PSO variant combining population-diversity monitoring and differential mutation is developed to jointly tune the 15-dimensional controller parameter vector. The practical finite-time reachability of the sliding variable and the uniform ultimate boundedness of the sliding variable and neural-weight estimation error are analyzed using a Lyapunov framework. Simulation results on a six-degree-of-freedom (6-DOF) robotic arm demonstrate improved tracking accuracy and disturbance-rejection performance, together with reduced high-frequency torque oscillations, compared with the evaluated baseline controllers. Full article

Buck converter power interfaces are commonly used as local voltage regulation units in compact actuator-driven microsystems, where the regulated voltage needs to remain stable under input, load, and circuit-level disturbances. In practical control loops, passive-parameter variations, external perturbations, and non-ideal sensor-driver dynamics may distort feedback signals, delay effective duty-cycle action, and degrade the transient response of conventional robust controllers. Motivated by this issue, this paper presents the analysis and design of a sensor-driver-aware integral nonsingular terminal sliding mode control (INTSMC) method for a buck converter power interface under multi-source disturbances. A control-oriented averaged model is first constructed by incorporating converter parameter perturbations, load-side disturbances, Hall sensor dynamics, and isolated driver characteristics into a unified representation. Based on this model, an integral nonsingular terminal sliding surface is designed to improve voltage tracking performance while avoiding singularity in the reaching process. The corresponding control law is further arranged in a pulse-width modulation-realizable duty-cycle form, making it suitable for digital converter control. In addition, a phase-trajectory-based response-time estimation method is introduced to analyze the influence of initial states, disturbance levels, and hardware dynamic parameters on the closed-loop reaching behavior. Simulation studies under different operating conditions are carried out to evaluate the proposed controller. Simulation and experimental results show that the proposed method achieves a settling time within 33 ms, a steady-state voltage error within 0.01 V, and a measured efficiency of 83.5%~88.9%, indicating its feasibility for robust power regulation in micro-actuator-oriented microsystems where sensor-driver dynamics cannot be ignored. Full article

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (${\gamma }_1=0.4$, ${\gamma }_2=0.2$, ${\gamma }_3=0.1$) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

In the anti-sway control of variable-length double-pendulum gantry cranes, traditional sliding mode control relies on high switching gains, which can cause chattering. Additionally, the introduction of neural networks presents challenges in tuning high-dimensional parameters. To address these issues, this study proposes an adaptive hierarchical sliding mode control strategy based on an RBF neural network and particle swarm optimization. First, a low-energy-dissipation dynamic model is established without the small-angle assumption. Second, a composite hierarchical sliding surface is designed to achieve multi-objective decoupling, and an RBF neural network is utilized to approximate the system’s unknown dynamics online, thereby reducing switching gains and suppressing chattering. The asymptotic stability of the closed-loop system is proven based on Lyapunov theory. Finally, a particle swarm optimization algorithm is introduced to achieve automated, high-precision matching of high-dimensional controller parameters. Simulation results indicate that the control method designed in this paper can achieve automatic matching of high-dimensional parameters, effectively resolving the chattering issue in sliding mode control. Furthermore, under wide-range parameter perturbations and external multi-source disturbances, the controller exhibits strong robustness and demonstrates excellent positioning and anti-chattering performance. Full article

Electro-hydraulic servo systems are widely used in industrial machinery and automation due to their high power density and fast dynamic response; however, their achievable positioning accuracy is often limited by nonlinear friction effects. In many robust control strategies, including sliding mode control and its multi-surface variants, friction is commonly treated as a lumped bounded disturbance. This simplification neglects the dynamic and operating condition-dependent nature of friction, leaving the practical value of explicit friction compensation insufficiently clarified, especially for electro-hydraulic actuators operating near their bandwidth limits. This paper presents an experimental evaluation of LuGre-based dynamic friction compensation integrated into a multi-surface sliding mode control framework for electro-hydraulic actuators. Rather than proposing a new control methodology, the study focuses on clarifying, from a control-oriented mechanical engineering perspective, how friction compensation influences closed-loop tracking performance under different operating regimes. The proposed scheme is implemented on a laboratory-scale electro-hydraulic test bench and evaluated using step and sinusoidal reference motions over a wide range of excitation frequencies, from low-speed operation to the practical bandwidth limit of the actuator. Comparative experiments with a conventional proportional–integral–derivative controller and a multi-surface sliding mode controller without friction compensation are conducted to isolate the effect of explicit friction modeling. The experimental results reveal a strongly frequency-dependent influence of friction on tracking performance. At low excitation frequencies (e.g., 0.1 Hz), friction compensation provides only marginal improvement in root mean square (RMS) tracking errors. In contrast, as the excitation frequency approaches the actuator bandwidth limit (1 Hz), explicit LuGre-based friction compensation reduces the relative RMS tracking error by approximately 57% compared with the baseline MSSM controller and by up to 82% relative to a conventional PID controller. These results demonstrate that the effectiveness of friction compensation is highly dependent on operating conditions, providing experimentally grounded guidance for the design of control strategies for bandwidth-limited electro-hydraulic machines. Full article

This paper proposes a novel prescribed-time formation tracking control farmework of multi-fixed-wing UAVs under external disturbance and actuator failures. As the complexity of aerial missions intensifies, achieving precise position and attitude tracking within a user-defined upper bound of settling time becomes a paramount challenge for intelligent swarm systems. Unlike traditional finite or fixed-time methods, where convergence depends on initial states or suffers from conservative estimation, the proposed approach ensures stability within a prescribed time independent of initial conditions. A key innovation is the introduction of a piecewise reference convergence differential function. This mechanism eliminates the need for state transitions, thereby reducing computational complexity while ensuring smooth tracking without control surface chattering across the entire mission. Additionally, a prescribed-time sliding mode disturbance observer is developed to provide precise and timely compensation for external disturbances and actuator faults. Rigorous Lyapunov analysis proves that all closed-loop signals are bounded and the tracking errors converge to a small neighborhood of zero within the predefined time. Numerical simulations demonstrate that, under time-varying disturbances and actuator faults, the disturbance estimation errors converge within 4 s, while both attitude and velocity tracking errors converge within 6 s, achieving fast transient response and high tracking accuracy. The UAV swarm successfully maintains the desired formation during aggressive maneuvers, including speed variations, climbing, and diving. These results verify that the proposed method provides a computationally efficient, robust, and high-precision solution for time-critical formation control of fixed-wing UAV swarms under complex uncertainties. Full article

This paper proposes a finite-time target tracking control for multi-rotor unmanned aerial vehicles (UAVs) based on a vision-inertial-stabilized platform. To address the challenge of stable and accurate moving target tracking, the sliding mode control (SMC) technique is used to overcome limitations of conventional control algorithms, such as poor robustness and slow convergence speed. First, by computing the pixel deviation between the target and the image center, a kinematic model of the tracking target is established. Then, by introducing homogeneous system theory into the sliding mode surface design, a non-singular fast integral terminal sliding mode control (NFITSMC) is designed for target tracking via regulating the rotational angular acceleration of dual actuators in the vision inertial stabilized platform, thereby driving the pixel deviation to converge to zero in a finite time. Strict theoretical analysis is given to prove the finite-time stability and robustness of the closed-loop control system. Furthermore, simulation results demonstrate that the proposed method maintains higher tracking accuracy than SMC, ISMC, and TSMC. Full article

To address the deteriorated control performance of permanent magnet synchronous motor (PMSM) drive systems for new energy vehicles (NEVs) under complex conditions caused by multi-source disturbances (internal parameter perturbations and external load mutations), this paper proposes an improved terminal integral sliding mode control (ITISMC-ADERL) strategy integrating a piecewise adaptive terminal integral sliding mode surface and an ADERL. The proposed sliding mode surface adopts interval-adaptive switching between high- and low-order power terms, completely eliminating singularity and integral saturation defects of traditional terminal sliding mode control while ensuring fast convergence, and achieving an optimal structural balance between convergence speed and chattering suppression. The state-dependent ADERL leverages the synergy of error-sliding variable coupled dynamic gain adjustment and variable exponential power compensation, realizing dual-mode adaptive switching of “strong driving for fast approaching far from the sliding surface, weak gain for smooth regulation near the sliding surface”, which significantly improves control accuracy and anti-disturbance robustness. The finite-time convergence of the closed-loop system is rigorously proved via Lyapunov stability theory. Full-operating-condition comparative tests on a TMS320F28379D DSP platform show that the proposed strategy outperforms SMC-ERL, ISMC-ERL and ITISMC-ERL in all test scenarios (no-load startup, acceleration/deceleration, sudden load changes, flux linkage perturbation), meeting the requirements of high-performance NEV drive systems and possessing important engineering application potential. Full article

This paper presents a finite-time uniformly ultimately bounded (FTUUB) cooperative control strategy based on a nonlinear disturbance observer (NDOB) for high-precision collaborative control of multi-hydraulic robotic arm systems operating under unknown disturbances and model uncertainties in confined scenarios such as coal silo cleaning. The proposed approach simplifies control design by lumping various uncertainties into a total disturbance, which is estimated and compensated in real time by the NDOB. Building upon this, a finite-time convergent sliding mode controller is developed, wherein the disturbance compensation is inherently embedded, ensuring that both position and velocity tracking errors converge to a small neighborhood of zero within a finite time. A master–slave distributed control architecture is adopted, with the agent communication topology characterized by graph theory. To mitigate the chattering inherent in traditional sliding mode control, a smooth hyperbolic tangent function is employed to construct the sliding surface. Rigorous Lyapunov stability analysis demonstrates that the closed-loop system achieves uniform ultimate boundedness within a finite time. Comprehensive simulation experiments, including a digital twin-based visualization in a virtual coal silo environment, validate the superior performance of the proposed method in terms of tracking accuracy, convergence speed, disturbance rejection, and control smoothness. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

To address the high energy consumption and low efficiency in shredding film–stalk mixtures during the resource utilization of cotton-field residues in Xinjiang—issues arising from the large mechanical-property differences among the mixture components—a custom single-support shearing fixture was developed to investigate the effects of residual-film wrapping layers, blade rake angle, sliding-cutting angle, and shearing speed on the F_j^max. Based on a Box–Behnken response surface design combined with analysis of variance and microscopic observations of the shearing process, the results showed that all main-effect factors had extremely significant influences on the F_j^max (p < 0.0001). Their relative contributions followed the following order: number of film wrapping layers > blade rake angle > shearing speed > sliding-cutting angle. Residual-film wrapping markedly increased shear resistance; increasing the sliding-cutting angle effectively reduced the shearing force; and reducing the rake angle facilitated more energy-efficient shredding. Interaction analysis further revealed significant coupling between sliding-cutting angle and shearing speed, rake angle and sliding-cutting angle, and rake angle and shearing speed (p < 0.05). Comparative shearing tests indicated that pure cotton stalks exhibited continuous brittle fracture with relatively stable force–displacement profiles, whereas film–stalk composites showed a sequentially coupled failure mode characterized by “residual-film pre-shearing–primary stalk fracture–secondary film stretching,” leading to multi-peak fluctuations in the force–displacement curves. Based on response surface optimization and mechanistic analysis, a parameter combination of a 35° rake angle, a 4–8° sliding-cutting angle, and medium-to-low shearing speed is recommended for shredding operations. This study elucidates the shearing and fragmentation mechanisms of film–stalk mixtures, provides theoretical guidance for optimizing key structural and operational parameters of post-recovery equipment, and offers important engineering value for promoting farmland residual-film pollution control and agricultural waste resource utilization. Full article

Sliding mode control (SMC) is a robust nonlinear control framework that enforces system trajectories onto predefined manifolds, providing strong robustness guarantees against uncertainties. However, SMC inherently introduces unwanted transients or chattering in system state trajectories, which may cause issues especially for sensitive applications such as regulation of drug administration. This paper proposes a multi-input smooth sliding mode control (MISSMC) strategy that combines radiotherapy and chemotherapy for a nonlinear tumor–immune dynamical system described by ordinary differential equations. The closed-loop system is first analyzed to establish key qualitative properties: all state variables remain positive and bounded, the sliding surfaces exhibit asymptotic convergence, and explicit analytical upper bounds on the cumulative therapy doses are derived under clinically motivated constraints. On this basis, a smooth hyperbolic-tangent sliding manifold and associated control law are designed to regulate the radiation and drug infusion rates. While the use of a hyperbolic-tangent smoothing function effectively suppresses chattering, it introduces a small steady-state error due to the presence of a boundary layer. To address this limitation, integral action is incorporated into the sliding surfaces, ensuring asymptotic convergence of tumor state and reducing residual steady-state error, while enhancing robustness against model uncertainties and parameter variations. Numerical simulations, based on a brain-tumor case study, show that the proposed smooth SMC markedly suppresses transient overshoots in both states and control inputs, while preserving effective tumor reduction. Compared with a conventional (non-smooth) SMC scheme, the MISSMC controller reduces baseline radiation and chemotherapy intensities on average by roughly 70%. Similarly, MISSMC lowers the overall cumulative doses on average by about 40%, without degrading the therapeutic outcome. The resulting integral smooth SMC framework therefore offers a rigorous nonlinear-systems approach to designing combined radio-chemotherapy protocols with guaranteed positivity, boundedness, and asymptotic stabilization of the closed-loop system, together with explicit bounds on the control inputs. Full article

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around ${10}^{-2}$, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics. Full article